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Packard, Norman H. Chialvo, Dante Hinrichs, Brant Eric 2011-05-07T13:49:18Z 2011-05-07T13:49:18Z 10000-01-01 1994 This thesis concerns characterizing complex time series from the scaling of prediction error. We use the global modeling technique of radial basis function approximation to build models from a state-space reconstruction of a time series that otherwise appears complicated or random (i.e. aperiodic, irregular). Prediction error as a function of prediction horizon is obtained from the model using the direct method. The relationship between the underlying dynamics of the time series and the logarithmic scaling of prediction error as a function of prediction horizon is investigated. We use this relationship to characterize the dynamics of both a model chaotic system and physical data from the optic tectum of an attentive pigeon exhibiting the important phenomena of nonstationary neuronal oscillations in response to visual stimuli. Made available in DSpace on 2011-05-07T13:49:18Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9512394.pdf: 3955971 bytes, checksum: 159f76d68de795de259af8c37d062a61 (MD5) Previous issue date: 1994 Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:59:33Z Item is restricted indefinitely. Restriction data tranferred 2014-07-01T11:28:06-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission ETDs are only available to UIUC Users without author permission U of I Only AAI9512394 (UMI)AAI9512394 http://hdl.handle.net/2142/22722 eng Copyright 1994 Hinrichs, Brant Eric Biology, Biostatistics Statistics Physics, General Characterizing complex time-series from the scaling of prediction error text Physics Physics University of Illinois at Urbana-Champaign Dissertation Ph.D.